Within the OSNs generated by users, community structures form naturally, and research into their
detection is very active [1]}, [2]}. These developments in community detection have produced a diverse set of methods which are at our disposal.
Run times of the algorithms are a major concern,
and current datasets
can be too large for many of the algorithms available.
One approach to deal with the size is by using network samples; for example,
[3]} analyzes community structure
in a subset of millions of nodes taken from Facebook.
However, for the types of effects that span over the entirety of the networks, we wish to avoid sampling and deal with complete networks.
